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reprojecting xarray dataset: TypeError: only length-1 arrays can be converted to Python scalars
I'm trying to reproject ERA5 data from lon/lat (EPSG:4326) to New Zealand Transverse Mercator (EPSG:2193). I'm getting "TypeError: only length-1 arrays can be converted to Python scalars" from reproject. As far as I can tell my usage and data are quite analogous to this example in the documentation.
Code Sample, a copy-pastable example if possible
A "Minimal, Complete and Verifiable Example" will make it much easier for maintainers to help you: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
In [4]: data_out
Out[4]:
<xarray.DataArray (time: 2, y: 2400, x: 2400)> Size: 92MB
array([[[ 0. , 0. , 0. , ...,
0. , 0. , 0. ],
[ 0. , 0. , 0. , ...,
0. , 0. , 0. ],
[ 0. , 0. , 0. , ...,
0. , 0. , 0. ],
...,
[3102745.32245305, 3099395.65992008, 3096046.04169629, ...,
0. , 0. , 0. ],
[3097108.93459378, 3093759.06215556, 3090409.23388688, ...,
0. , 0. , 0. ],
[3091471.03450028, 3088120.95185706, 3084770.91324366, ...,
0. , 0. , 0. ]],
[[ 0. , 0. , 0. , ...,
0. , 0. , 0. ],
[ 0. , 0. , 0. , ...,
0. , 0. , 0. ],
[ 0. , 0. , 0. , ...,
0. , 0. , 0. ],
...,
[3281400.8388549 , 3278053.05613346, 3274705.25451338, ...,
0. , 0. , 0. ],
[3274617.22569414, 3271274.50480555, 3267931.76674866, ...,
0. , 0. , 0. ],
[3267852.4005159 , 3264514.74535506, 3261177.0747575 , ...,
0. , 0. , 0. ]]])
Coordinates:
* time (time) datetime64[ns] 16B 2022-01-01 2022-01-01T01:00:00
x (y, x) float64 46MB 150.1 150.1 150.1 ... -177.3 -177.3 -177.3
y (y, x) float64 46MB -30.0 -30.0 -30.0 ... -40.0 -40.0 -40.0
spatial_ref int64 8B 0
Attributes:
regrid_method: bilinear
In [5]: data_out.spatial_ref
Out[5]:
<xarray.DataArray 'spatial_ref' ()> Size: 8B
array(0)
Coordinates:
spatial_ref int64 8B 0
Attributes:
crs_wkt: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["...
semi_major_axis: 6378137.0
semi_minor_axis: 6356752.314245179
inverse_flattening: 298.257223563
reference_ellipsoid_name: WGS 84
longitude_of_prime_meridian: 0.0
prime_meridian_name: Greenwich
geographic_crs_name: WGS 84
horizontal_datum_name: World Geodetic System 1984
grid_mapping_name: latitude_longitude
spatial_ref: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["...
In [6]: data_out_nztm = data_out.rio.reproject('EPGS:2193') [20/4699]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 data_out_nztm = data_out.rio.reproject('EPGS:2193')
File ~/mambaforge/envs/pyVPRM3/lib/python3.12/site-packages/rioxarray/raster_array.py:468, in RasterArray.reproject(self, dst_crs, resolution, shape, transform, resampling,
nodata, **kwargs)
461 kwargs.setdefault("gcps", gcps)
463 use_affine = (
464 "gcps" not in kwargs
465 and "rpcs" not in kwargs
466 and "src_geoloc_array" not in kwargs
467 )
--> 468 src_affine = None if not use_affine else self.transform(recalc=True)
469 if transform is None:
470 dst_affine, dst_width, dst_height = _make_dst_affine(
471 src_data_array=self._obj,
472 src_crs=self.crs,
(...) 476 **kwargs,
477 )
File ~/mambaforge/envs/pyVPRM3/lib/python3.12/site-packages/rioxarray/rioxarray.py:707, in XRasterBase.transform(self, recalc)
704 return transform
706 try:
--> 707 src_left, _, _, src_top = self._unordered_bounds(recalc=recalc)
708 src_resolution_x, src_resolution_y = self.resolution(recalc=recalc)
709 except (DimensionMissingCoordinateError, DimensionError):
File ~/mambaforge/envs/pyVPRM3/lib/python3.12/site-packages/rioxarray/rioxarray.py:1079, in XRasterBase._unordered_bounds(self, recalc)
1062 def _unordered_bounds(
1063 self, recalc: bool = False
1064 ) -> tuple[float, float, float, float]:
1065 """
1066 Unordered bounds.
1067
(...) 1077 Outermost coordinates of the `xarray.DataArray` | `xarray.Dataset`.
1078 """
-> 1079 resolution_x, resolution_y = self.resolution(recalc=recalc)
1081 try:
1082 # attempt to get bounds from xarray coordinate values
1083 left, bottom, right, top = self._internal_bounds()
File ~/mambaforge/envs/pyVPRM3/lib/python3.12/site-packages/rioxarray/rioxarray.py:1046, in XRasterBase.resolution(self, recalc)
1043 # if the coordinates of the spatial dimensions are missing
1044 # use the cached transform resolution
1045 try:
-> 1046 left, bottom, right, top = self._internal_bounds()
1047 except DimensionMissingCoordinateError:
1048 if transform is None:
File ~/mambaforge/envs/pyVPRM3/lib/python3.12/site-packages/rioxarray/rioxarray.py:1009, in XRasterBase._internal_bounds(self)
1007 raise DimensionMissingCoordinateError(f"{self.y_dim} missing coordinates.")
1008 try:
-> 1009 left = float(self._obj[self.x_dim][0])
1010 right = float(self._obj[self.x_dim][-1])
1011 top = float(self._obj[self.y_dim][0])
File ~/mambaforge/envs/pyVPRM3/lib/python3.12/site-packages/xarray/core/common.py:157, in AbstractArray.__float__(self)
156 def __float__(self: Any) -> float:
--> 157 return float(self.values)
TypeError: only length-1 arrays can be converted to Python scalars
Problem description
The reprojection results in a TypeError exception.
Expected Output
I expected an xarray dataset in reprojected coordinates.
Environment Information
In [7]: rioxarray.show_versions()
rioxarray (0.18.2) deps:
rasterio: 1.4.3
xarray: 2025.1.2
GDAL: 3.10.2
GEOS: 3.13.0
PROJ: 9.5.1
PROJ DATA: /home/timh/mambaforge/envs/pyVPRM3/share/proj
GDAL DATA: /home/timh/mambaforge/envs/pyVPRM3/share/gdal
Other python deps:
scipy: 1.15.2
pyproj: 3.7.1
System:
python: 3.12.9 | packaged by conda-forge | (main, Mar 4 2025, 22:48:41) [GCC 13.3.0]
executable: /home/timh/mambaforge/envs/pyVPRM3/bin/python3.12
machine: Linux-4.18.0-553.8.1.el8_10.x86_64-x86_64-with-glibc2.28
Installation method
- installed by conda
Conda environment information (if you installed with conda):
Environment (
conda list):
# packages in environment at /home/timh/mambaforge/envs/pyVPRM3:
#
# Name Version Build Channel
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uuid 1.30 pypi_0 pypi
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x265 3.5 h924138e_3 conda-forge
xarray 2025.1.2 pyhd8ed1ab_0 conda-forge
xcb-util 0.4.1 hb711507_2 conda-forge
xcb-util-cursor 0.1.5 hb9d3cd8_0 conda-forge
xcb-util-image 0.4.0 hb711507_2 conda-forge
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xcb-util-renderutil 0.3.10 hb711507_0 conda-forge
xcb-util-wm 0.4.2 hb711507_0 conda-forge
xerces-c 3.2.5 h988505b_2 conda-forge
xesmf 0.8.8 pyhd8ed1ab_1 conda-forge
xkeyboard-config 2.43 hb9d3cd8_0 conda-forge
xorg-libice 1.1.2 hb9d3cd8_0 conda-forge
xorg-libsm 1.2.5 he73a12e_0 conda-forge
xorg-libx11 1.8.11 h4f16b4b_0 conda-forge
xorg-libxau 1.0.12 hb9d3cd8_0 conda-forge
xorg-libxcomposite 0.4.6 hb9d3cd8_2 conda-forge
xorg-libxcursor 1.2.3 hb9d3cd8_0 conda-forge
xorg-libxdamage 1.1.6 hb9d3cd8_0 conda-forge
xorg-libxdmcp 1.1.5 hb9d3cd8_0 conda-forge
xorg-libxext 1.3.6 hb9d3cd8_0 conda-forge
xorg-libxfixes 6.0.1 hb9d3cd8_0 conda-forge
xorg-libxi 1.8.2 hb9d3cd8_0 conda-forge
xorg-libxmu 1.2.1 hb9d3cd8_1 conda-forge
xorg-libxrandr 1.5.4 hb9d3cd8_0 conda-forge
xorg-libxrender 0.9.12 hb9d3cd8_0 conda-forge
xorg-libxt 1.3.1 hb9d3cd8_0 conda-forge
xorg-libxtst 1.2.5 hb9d3cd8_3 conda-forge
xorg-libxxf86vm 1.1.6 hb9d3cd8_0 conda-forge
xyzservices 2025.1.0 pyhd8ed1ab_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
yaml-cpp 0.8.0 h59595ed_0 conda-forge
yarl 1.18.3 py312h178313f_1 conda-forge
zeromq 4.3.5 h3b0a872_7 conda-forge
zict 3.0.0 pyhd8ed1ab_1 conda-forge
zipp 3.21.0 pyhd8ed1ab_1 conda-forge
zlib 1.3.1 hb9d3cd8_2 conda-forge
zstandard 0.23.0 py312hef9b889_1 conda-forge
zstd 1.5.6 ha6fb4c9_0 conda-forge
$ conda list | grep -E "rasterio|xarray|gdal"
cf_xarray 0.10.0 pyhd8ed1ab_2 conda-forge
libgdal-core 3.10.2 h3359108_0 conda-forge
rasterio 1.4.3 py312h8cae83d_0 conda-forge
rioxarray 0.18.2 pyhd8ed1ab_0 conda-forge
xarray 2025.1.2 pyhd8ed1ab_0 conda-forge
Details about
conda and system ( conda info ):
$ conda info
active environment : pyVPRM3
active env location : /home/timh/mambaforge/envs/pyVPRM3
shell level : 2
user config file : /home/timh/.condarc
populated config files : /home/timh/mambaforge/.condarc
conda version : 24.9.0
conda-build version : 24.5.1
python version : 3.9.16.final.0
solver : libmamba (default)
virtual packages : __archspec=1=cascadelake
__conda=24.9.0=0
__glibc=2.28=0
__linux=4.18.0=0
__unix=0=0
base environment : /home/timh/mambaforge (writable)
conda av data dir : /home/timh/mambaforge/etc/conda
conda av metadata url : None
channel URLs : https://conda.anaconda.org/conda-forge/linux-64
https://conda.anaconda.org/conda-forge/noarch
package cache : /home/timh/mambaforge/pkgs
/home/timh/.conda/pkgs
envs directories : /home/timh/mambaforge/envs
/home/timh/.conda/envs
platform : linux-64
user-agent : conda/24.9.0 requests/2.31.0 CPython/3.9.16 Linux/4.18.0-553.8.1.el8_10.x86_64 rhel/8.10 glibc/2.28 solver/libmamba conda-libmamba-solver/23.11.1 libmambapy/1.5.8
UID:GID : 3678:3678
netrc file : None
offline mode : False
This happens with any ERA data?
@RichardScottOZ, I don't know, honestly, I've only tried with the ERA5 data I am trying to use. I've regridded to a higher resolution in a previous step as well using xesmf. I suspect the problem is something to do with the structure of or metadata in the xarray dataset that xesmf produces, but 1) I don't know that for sure and 2) I'm a bit at a loss for what the problem might be.
Another possibility is the 180th meridian is messing something up. I'm working in New Zealand, and ERA5 and MODIS are tiled such that the datasets usually span the 180th meridian. That discontinuity in longitude has thrown unexpected wrenches into the gears with other tools.
I'm happy to post a netcdf file of the data I'm trying to reproject if that is helpful.
Looks like you have location array based data, x/y coords are 2d, but code that raises the error expects to see 1d x/y coords.
Does this work:
xds_1d = xds.rio.reproject(
"EPSG:2193",
src_geoloc_array=(
xds.coords["x"].values,
xds.coords["y"].values,
),
georeferencing_convention="PIXEL_CENTER",
)
Looks like you have location array based data, x/y coords are 2d, but code that raises the error expects to see 1d x/y coords.
That's true, yes... This particular dataset cannot be described with 1-dimensional X and Y coordinates (in its current projection, at least).
Does this work:
xds_1d = xds.rio.reproject( "EPSG:2193", src_geoloc_array=( xds.coords["x"].values, xds.coords["y"].values, ),
georeferencing_convention="PIXEL_CENTER", )
@snowman2 it does not, unfortunately... It tries to allocate a 300+ TB array and then fails with a memory error :(
In [25]: data_out
Out[25]:
<xarray.DataArray (time: 2, y: 2400, x: 2400)> Size: 92MB
array([[[ 0. , 0. , 0. , ...,
<... snip ...>
0. , 0. , 0. ]]])
Coordinates:
* time (time) datetime64[ns] 16B 2022-01-01 2022-01-01T01:00:00
x (y, x) float64 46MB 150.1 150.1 150.1 ... -177.3 -177.3 -177.3
y (y, x) float64 46MB -30.0 -30.0 -30.0 ... -40.0 -40.0 -40.0
spatial_ref int64 8B 0
Attributes:
regrid_method: bilinear
In [26]: xds_1d = data_out.rio.reproject(
...: "EPSG:2193",
...: src_geoloc_array=(
...: data_out.coords["x"].values,
...: data_out.coords["y"].values,
...: ),
...: georeferencing_convention="PIXEL_CENTER",
...: )
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
Cell In[26], line 1
----> 1 xds_1d = data_out.rio.reproject(
2 "EPSG:2193",
3 src_geoloc_array=(
4 data_out.coords["x"].values,
5 data_out.coords["y"].values,
6 ),
7 georeferencing_convention="PIXEL_CENTER",
8 )
File ~/mambaforge/envs/pyVPRM3/lib/python3.12/site-packages/rioxarray/raster_array.py:485, in RasterArray.reproject(self, dst_crs, resolution, shape, transform, resampling, nodata, **kwargs)
482 else:
483 dst_height, dst_width = self.shape
--> 485 dst_data = self._create_dst_data(dst_height=dst_height, dst_width=dst_width)
487 dst_nodata = self._get_dst_nodata(nodata)
488 rasterio.warp.reproject(
489 source=self._obj.values,
490 destination=dst_data,
(...) 498 **kwargs,
499 )
File ~/mambaforge/envs/pyVPRM3/lib/python3.12/site-packages/rioxarray/raster_array.py:542, in RasterArray._create_dst_data(self, dst_height, dst_width)
540 extra_dim = self._check_dimensions()
541 if extra_dim:
--> 542 dst_data = numpy.zeros(
543 (self._obj[extra_dim].size, dst_height, dst_width),
544 dtype=self._obj.dtype.type,
545 )
546 else:
547 dst_data = numpy.zeros((dst_height, dst_width), dtype=self._obj.dtype.type)
MemoryError: Unable to allocate 317. TiB for an array with shape (2, 2795031, 7800467) and data type float64
My sense from the documentation is that this array is within the intended use cases (that is, an xarray dataset with 2-dimensional X and Y grid locations roughly 2000 rows and columns. Is that accurate?
@Timothy-W-Hilton looks like this code can't auto-guess reasonable destination geobox:
https://github.com/corteva/rioxarray/blob/564c946d07058b8ebdc86b1751ba24169cd6c6ef/rioxarray/raster_array.py#L154-L160
you can specify destination raster geo referencing fully with transform=,shape=,dst_crs=, or you can play around with resolution= parameter and see if that fixes anything, although it's probably due to lon=180 crossing.
This is likely a helpful reference: https://github.com/corteva/rioxarray/issues/58